Thank you for contributing to LangChain!
- Oracle AI Vector Search
Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
- Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
This Pull Requests Adds the following functionalities
Oracle AI Vector Search : Vector Store
Oracle AI Vector Search : Document Loader
Oracle AI Vector Search : Document Splitter
Oracle AI Vector Search : Summary
Oracle AI Vector Search : Oracle Embeddings
- We have added unit tests and have our own local unit test suite which
verifies all the code is correct. We have made sure to add guides for
each of the components and one end to end guide that shows how the
entire thing runs.
- We have made sure that make format and make lint run clean.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com>
Co-authored-by: hroyofc <harichandan.roy@oracle.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**: ToolKit and Tools for accessing data in a Cassandra
Database primarily for Agent integration. Initially, this includes the
following tools:
- `cassandra_db_schema` Gathers all schema information for the connected
database or a specific schema. Critical for the agent when determining
actions.
- `cassandra_db_select_table_data` Selects data from a specific keyspace
and table. The agent can pass paramaters for a predicate and limits on
the number of returned records.
- `cassandra_db_query` Expiriemental alternative to
`cassandra_db_select_table_data` which takes a query string completely
formed by the agent instead of parameters. May be removed in future
versions.
Includes unit test and two notebooks to demonstrate usage.
**Dependencies**: cassio
**Twitter handle**: @PatrickMcFadin
---------
Co-authored-by: Phil Miesle <phil.miesle@datastax.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
* Groundedness Check takes `str` or `list[Document]` as input.
* Deprecate `GroundednessCheck` due to its naming.
* Added `UpstageGroundednessCheck`.
* Hotfix for Groundedness Check parameter.
The name `query` was misleading and it should be `answer` instead.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
docs: Fix link for `partition_pdf` in Semi_Structured_RAG.ipynb cookbook
- **Description:** Fix incorrect link to unstructured-io `partition_pdf`
section
core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]
```python
class ToolCall(TypedDict):
name: str
args: Dict[str, Any]
id: Optional[str]
class InvalidToolCall(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
error: Optional[str]
class ToolCallChunk(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
index: Optional[int]
class AIMessage(BaseMessage):
...
tool_calls: List[ToolCall] = []
invalid_tool_calls: List[InvalidToolCall] = []
...
class AIMessageChunk(AIMessage, BaseMessageChunk):
...
tool_call_chunks: Optional[List[ToolCallChunk]] = None
...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
- additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).
Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: Add semantic caching and memory using
MongoDB"
- [ ] **PR message**:
- **Description:** This PR introduces functionality for adding semantic
caching and chat message history using MongoDB in RAG applications. By
leveraging the MongoDBCache and MongoDBChatMessageHistory classes,
developers can now enhance their retrieval-augmented generation
applications with efficient semantic caching mechanisms and persistent
conversation histories, improving response times and consistency across
chat sessions.
- **Issue:** N/A
- **Dependencies:** Requires `datasets`, `langchain`,
`langchain-mongodb`, `langchain-openai`, `pymongo`, and `pandas` for
implementation. MongoDB Atlas is used for database services, and the
OpenAI API for model access.
- **Twitter handle:** @richmondalake
Co-authored-by: Erick Friis <erick@langchain.dev>
Last year Microsoft [changed the
name](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
of Azure Cognitive Search to Azure AI Search. This PR updates the
Langchain Azure Retriever API and it's associated docs to reflect this
change. It may be confusing for users to see the name Cognitive here and
AI in the Microsoft documentation which is why this is needed. I've also
added a more detailed example to the Azure retriever doc page.
There are more places that need a similar update but I'm breaking it up
so the PRs are not too big 😄 Fixing my errors from the previous PR.
Twitter: @marlene_zw
Two new tests added to test backward compatibility in
`libs/community/tests/integration_tests/retrievers/test_azure_cognitive_search.py`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: Video imagery to text (Closed Captioning)
This pull request introduces the VideoCaptioningChain, a tool for
automated video captioning. It processes audio and video to generate
subtitles and closed captions, merging them into a single SRT output.
Issue: https://github.com/langchain-ai/langchain/issues/11770
Dependencies: opencv-python, ffmpeg-python, assemblyai, transformers,
pillow, torch, openai
Tag maintainer:
@baskaryan
@hwchase17
Hello! We are a group of students from the University of Toronto
(@LunarECL, @TomSadan, @nicoledroi1, @A2113S) that want to make a
contribution to the LangChain community! We have ran make format, make
lint and make test locally before submitting the PR. To our knowledge,
our changes do not introduce any new errors.
Thank you for taking the time to review our PR!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This patch updates multiple function "run" to "invoke" in
llm_symbolic_math.ipynb.
Without this patch, you see following message.
The function `run` was deprecated in LangChain 0.1.0
and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
- **Description:** Add support for Intel Lab's [Visual Data Management
System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store
- **Dependencies:** `vdms` library which requires protobuf = "4.24.2".
There is a conflict with dashvector in `langchain` package but conflict
is resolved in `community`.
- **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe)
- **Added tests:**
libs/community/tests/integration_tests/vectorstores/test_vdms.py
- **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb
- **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [ ] **cookbook** - update example for SalesGPT - include Stripe
Payment Link Generation
- **Description:** We updated the Jupyter notebook example with the
ability of the AI Agent to negotiate with customers and then close the
deal by generating a custom Stripe payment link.
- **Issue:** N/A
- **Dependencies:** N/a
- **Twitter handle:** @FilipMichalsky @0xtotaylor
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Filip Michalsky <filip_michalsky@g.harvard.edu>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Update Azure Document Intelligence implementation by
Microsoft team and RAG cookbook with Azure AI Search
---------
Co-authored-by: Lu Zhang (AI) <luzhan@microsoft.com>
Co-authored-by: Yateng Hong <yatengh@microsoft.com>
Co-authored-by: teethache <hongyateng2006@126.com>
Co-authored-by: Lu Zhang <44625949+luzhang06@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This patch updates function "run" to "invoke" in fake_llm.ipynb. Without
this patch, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
**Description:** Update stales link in Together AI documentation
**Issue:** Some links pointed to legacy webpages on the Together AI
website
**Dependencies:** None
**Lint and test**: `make format`, `make lint` were run
This PR is a successor to this PR -
https://github.com/langchain-ai/langchain/pull/17436
This PR updates the cookbook README with the notebook so that it is
available on langchain docs for discoverability.
cc: @baskaryan, @3coins
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This path updates function "run" to "invoke" in llm_bash.ipynb.
Without this path, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0
and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Follow up on https://github.com/langchain-ai/langchain/pull/17467.
- Update all references to the Elasticsearch classes to use the partners
package.
- Deprecate community classes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This patch updates function "run" to "invoke" in smart_llm.ipynb.
Without this patch, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Currently llm_checker.ipynb uses a function "run".
Update to "invoke" to avoid following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0
and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
This patch updates function "run" to "invoke".
Without this patch you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
- [ ] **PR title**: "cookbook: using Gemma on LangChain"
- [ ] **PR message**:
- **Description:** added a tutorial how to use Gemma with LangChain
(from VertexAI or locally from Kaggle or HF)
- **Dependencies:** langchain-google-vertexai==0.0.7
- **Twitter handle:** lkuligin
### Description
This PR moves the Elasticsearch classes to a partners package.
Note that we will not move (and later remove) `ElasticKnnSearch`. It
were previously deprecated.
`ElasticVectorSearch` is going to stay in the community package since it
is used quite a lot still.
Also note that I left the `ElasticsearchTranslator` for self query
untouched because it resides in main `langchain` package.
### Dependencies
There will be another PR that updates the notebooks (potentially pulling
them into the partners package) and templates and removes the classes
from the community package, see
https://github.com/langchain-ai/langchain/pull/17468
#### Open question
How to make the transition smooth for users? Do we move the import
aliases and require people to install `langchain-elasticsearch`? Or do
we remove the import aliases from the `langchain` package all together?
What has worked well for other partner packages?
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
